Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.Download PDF

29 Sept 2021, 00:35 (edited 28 Feb 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: case-based reasoning, interpretable machine learning, explainable artificial intelligence, xai, prototype learning
  • Abstract: Recent advances in machine learning have brought opportunities for the ever-increasing use of AI in the real world. This has created concerns about the black-box nature of many of the most recent machine learning approaches. In this work, we propose an interpretable neural network that leverages metric and prototype learning for classification tasks. It encodes its own explanations and provides an improved case-based reasoning through learning prototypes in an embedding space learned by a probabilistic nearest neighbor rule. Through experiments, we demonstrated the effectiveness of the proposed method in both performance and the accuracy of the explanations provided.
  • One-sentence Summary: Offering better prototype explanations using a nearest-neighbor friendly embedding space.
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